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Research map / Point clouds / Shape and surface completion

HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces

NeurIPS 2022 · Point clouds › Shape and surface completion · approach: Point cloud upsampling and normals · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use hyper surface fitting instead of 1-order jet fitting (ablation)HSurf-Net · PCPNet averageangular RMSE: 12.12 -> 10.11Helped
use hyper surface fitting instead of 2-order jet fitting (ablation)HSurf-Net · PCPNet averageangular RMSE: 12.75 -> 10.11Helped
use residual blocks instead of concatenation in fitting module (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.30 -> 10.11Helped
use Space Transformation instead of PointNet-based aggregation (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.61 -> 10.11Helped
add multi-scale Global Shift Layer vs fixed scale (ablation)HSurf-Net · PCPNet averageangular RMSE: 11.17 -> 10.11Helped
use learned relative position encoding instead of traditional encoding (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.29 -> 10.11Helped
add relative position encoding vs raw coordinates (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.42 -> 10.11Helped
add point weights in output module (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.71 -> 10.11Helped
add neighborhood consistency loss L_con (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.11 -> 10.29No clear effect
replace sin loss with MSE loss (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.11 -> 15.45Hurt
change patch size from 700 to 600 or 800 (ablation)HSurf-Net · PCPNet averageangular RMSE: 10.11 vs 10.16 (600), 10.15 (800)No clear effect

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